Prediction of human-virus protein-protein interactions through a sequence embedding-based machine learning method

Xiaodi Yang; Shiping Yang; Qinmengge Li; Stefan Wuchty; Ziding Zhang

doi:10.1016/j.csbj.2019.12.005

Prediction of human-virus protein-protein interactions through a sequence embedding-based machine learning method

Comput Struct Biotechnol J. 2019 Dec 26:18:153-161. doi: 10.1016/j.csbj.2019.12.005. eCollection 2020.

Authors

Xiaodi Yang¹, Shiping Yang², Qinmengge Li³, Stefan Wuchty^{4

5

6

7}, Ziding Zhang¹

Affiliations

¹ State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China.
² State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China.
³ National Demonstration Center for Experimental Biological Sciences Education, College of Biological Sciences, China Agricultural University, Beijing 100193, China.
⁴ Dept. of Computer Science, University of Miami, Miami, FL 33146, USA.
⁵ Dept. of Biology, University of Miami, Miami, FL 33146, USA.
⁶ Center of Computational Science, University of Miami, Miami, FL 33146, USA.
⁷ Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, USA.

Abstract

The identification of human-virus protein-protein interactions (PPIs) is an essential and challenging research topic, potentially providing a mechanistic understanding of viral infection. Given that the experimental determination of human-virus PPIs is time-consuming and labor-intensive, computational methods are playing an important role in providing testable hypotheses, complementing the determination of large-scale interactome between species. In this work, we applied an unsupervised sequence embedding technique (doc2vec) to represent protein sequences as rich feature vectors of low dimensionality. Training a Random Forest (RF) classifier through a training dataset that covers known PPIs between human and all viruses, we obtained excellent predictive accuracy outperforming various combinations of machine learning algorithms and commonly-used sequence encoding schemes. Rigorous comparison with three existing human-virus PPI prediction methods, our proposed computational framework further provided very competitive and promising performance, suggesting that the doc2vec encoding scheme effectively captures context information of protein sequences, pertaining to corresponding protein-protein interactions. Our approach is freely accessible through our web server as part of our host-pathogen PPI prediction platform (http://zzdlab.com/InterSPPI/). Taken together, we hope the current work not only contributes a useful predictor to accelerate the exploration of human-virus PPIs, but also provides some meaningful insights into human-virus relationships.

Keywords: AC, Auto Covariance; ACC, Accuracy; AUC, area under the ROC curve; AUPRC, area under the PR curve; Adaboost, Adaptive Boosting; CT, Conjoint Triad; Doc2vec; Embedding; Human-virus interaction; LD, Local Descriptor; MCC, Matthews correlation coefficient; ML, machine learning; MLP, Multiple Layer Perceptron; MS, mass spectroscopy; Machine learning; PPIs, protein-protein interactions; PR, Precision-Recall; Prediction; Protein-protein interaction; RBF, radial basis function; RF, Random Forest; ROC, Receiver Operating Characteristic; SGD, stochastic gradient descent; SVM, Support Vector Machine; Y2H, yeast two-hybrid.